1887
Volume 27, Issue 1
  • ISSN 0142-5471
  • E-ISSN: 1569-979X

Abstract

Abstract

Critical studies of data visualization often highlight how the reductive nature of visualization methods excludes data limitations and qualities that are crucial to understanding those data. This case study explores how a data visualization could express contingent, situated, and contextual facets of data. We examine how such data limitations might be surfaced and represented within visualizations through an interplay between the critique of an existing data visualization and the development of alternative designs. Based on a case study of urban tree data, we interrogate data limitations in relation to four different types of missingness: Incompleteness, Emptiness, Absence, and Nothingness. Our study enables reflections on how data limitations can be investigated using visualizations and considers the development of a critical visualization practice.

Available under the CC BY-NC 4.0 license.
Loading

Article metrics loading...

/content/journals/10.1075/idj.22006.hen
2022-11-07
2024-07-23
Loading full text...

Full text loading...

/deliver/fulltext/idj.22006.hen.html?itemId=/content/journals/10.1075/idj.22006.hen&mimeType=html&fmt=ahah

References

  1. Bivand, R., Keitt, T., & Rowlingson, B.
    (2021) rgdal: Bindings for the “Geospatial” Data Abstraction Library. https://CRAN.R-project.org/package=rgdal
    [Google Scholar]
  2. Davila, P.
    (2019) Diagrams of Power: Visualizing, mapping and performing resistance (01 edition). Eindhoven. Onomatopee.
    [Google Scholar]
  3. D’Ignazio, C.
    (2017) Creative data literacy: Bridging the gap between the data-haves and data-have nots. Information Design Journal, 23(1), 6–18. 10.1075/idj.23.1.03dig
    https://doi.org/10.1075/idj.23.1.03dig [Google Scholar]
  4. D’Ignazio, C., & Klein, L. F.
    (2016) Feminist Data Visualization. IEEE VIS Conference, Baltimore, October. 23–28.
    [Google Scholar]
  5. (2020) Data Feminism. MIT Press. 10.7551/mitpress/11805.001.0001
    https://doi.org/10.7551/mitpress/11805.001.0001 [Google Scholar]
  6. Dörk, M., Feng, P., Collins, C., & Carpendale, S.
    (2013) Critical InfoVis: Exploring the Politics of Visualization. CHI ’13 Extended Abstracts on Human Factors in Computing Systems, 2189–2198. 10.1145/2468356.2468739
    https://doi.org/10.1145/2468356.2468739 [Google Scholar]
  7. Dowle, M., & Srinivasan, A.
    (2021) data.table: Extension of `data.frame`. https://CRAN.R-project.org/package=data.table
    [Google Scholar]
  8. Drucker, J.
    (2011) Humanities approaches to graphical display. Digital Humanities Quarterly, 5(1), 1–21.
    [Google Scholar]
  9. (2017) Information visualization and/as enunciation. Journal of Documentation, 73(5), 903–916. 10.1108/JD‑01‑2017‑0004
    https://doi.org/10.1108/JD-01-2017-0004 [Google Scholar]
  10. Fernstad, S. J.
    (2019) To identify what is not there: A definition of missingness patterns and evaluation of missing value visualization. Information Visualization, 18(2), 230–250. 10.1177/1473871618785387
    https://doi.org/10.1177/1473871618785387 [Google Scholar]
  11. Geoportal Berlin [Google Scholar]
  12. Geoportal Berlin
    Geoportal Berlin. (n.d.b). Baumbestand Berlin – Straßenbäume. https://fbinter.stadt-berlin.de/fb/berlin/service_intern.jsp?id=s_wfs_baumbestand@senstadt&type=WFS
    [Google Scholar]
  13. Hall, P., Heath, C., & Coles-Kemp, L.
    (2015) Critical visualization: A case for rethinking how we visualize risk and security. Journal of Cybersecurity, 1(1), 93–108. 10.1093/cybsec/tyv004
    https://doi.org/10.1093/cybsec/tyv004 [Google Scholar]
  14. Hengesbach, N.
    (2022) Undoing Seamlessness: Exploring Seams for Critical Visualization. CHI Conference on Human Factors in Computing Systems Extended Abstracts, 2022, New Orleans, LA, USA. New York, NY, USA. 10.1145/3491101.3519703
    https://doi.org/10.1145/3491101.3519703 [Google Scholar]
  15. Hijmans, R. J.
    (2020) raster: Geographic Data Analysis and Modeling. https://CRAN.R-project.org/package=raster
    [Google Scholar]
  16. (2021) terra: Spatial Data Analysis. https://CRAN.R-project.org/package=terra
    [Google Scholar]
  17. Kay, M., Kola, T., Hullman, J., & Munson, S.
    (2016) When(ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems. ACM Human Factors in Computing Systems (CHI). idl.cs.washington.edu/papers/when-ish-is-my-bus
    [Google Scholar]
  18. Kennedy, H., Hill, R. L., Aiello, G., & Allen, W.
    (2016) The work that visualisation conventions do. Information, Communication & Society, 19(6), 715–735. 10.1080/1369118X.2016.1153126
    https://doi.org/10.1080/1369118X.2016.1153126 [Google Scholar]
  19. Kinkeldey, C., MacEachren, A. M., Riveiro, M., & Schiewe, J.
    (2017) Evaluating the effect of visually represented geodata uncertainty on decision-making: Systematic review, lessons learned, and recommendations. Cartography and Geographic Information Science, 44(1), 1–21. 10.1080/15230406.2015.1089792
    https://doi.org/10.1080/15230406.2015.1089792 [Google Scholar]
  20. Kitchin, R.
    (2014) The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1–14. 10.1007/s10708‑013‑9516‑8
    https://doi.org/10.1007/s10708-013-9516-8 [Google Scholar]
  21. Kosminsky, D., Walny, J., Vermeulen, J., Knudsen, S., Willett, W., & Carpendale, S.
    (2019) Belief at first sight: Data visualization and the rationalization of seeing. Information Design Journal, 25(1), 43–55.
    [Google Scholar]
  22. Kurgan, L.
    (2013) Close Up at a Distance: Mapping, Technology, and Politics. MIT Press. 10.2307/j.ctt14bs159
    https://doi.org/10.2307/j.ctt14bs159 [Google Scholar]
  23. Lockton, D., Ricketts, D., Aditya Chowdhury, S., & Lee, C. H.
    (2017) Exploring qualitative displays and interfaces. Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 1844–1852. 10.1145/3027063.3053165
    https://doi.org/10.1145/3027063.3053165 [Google Scholar]
  24. Loukissas, Y. A.
    (2016) A place for Big Data: Close and distant readings of accessions data from the Arnold Arboretum. Big Data & Society, 3(2), 2053951716661365. 10.1177/2053951716661365
    https://doi.org/10.1177/2053951716661365 [Google Scholar]
  25. (2019) All data are local: Thinking critically in a data-driven society. Cambridge, MA. The MIT Press. 10.7551/mitpress/11543.001.0001
    https://doi.org/10.7551/mitpress/11543.001.0001 [Google Scholar]
  26. Meyer, M., & Dykes, J.
    (2019) Criteria for Rigor in Visualization Design Study. IEEE Transactions on Visualization and Computer Graphics. 10.1109/TVCG.2019.2934539
    https://doi.org/10.1109/TVCG.2019.2934539 [Google Scholar]
  27. McCurdy, N., Gerdes, J., & Meyer, M.
    (2019) A Framework for Externalizing Implicit Error Using Visualization. IEEE Transactions on Visualization and Computer Graphics, 25(1), 925–935. 10.1109/TVCG.2018.2864913
    https://doi.org/10.1109/TVCG.2018.2864913 [Google Scholar]
  28. McInerny, G.
    (2018). Visualizing data: A view from design space. InRoutledge Handbook of Interdisciplinary Research Methods (pp.133–141). Routledge.
    [Google Scholar]
  29. McNutt, A., Kindlmann, G., & Correll, M.
    (2020) Surfacing Visualization Mirages. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. 10.1145/3313831.3376420
    https://doi.org/10.1145/3313831.3376420 [Google Scholar]
  30. Offenhuber, D.
    (2019) Data by Proxy – Material Traces as Autographic Visualizations. IEEE Transactions on Visualization and Computer Graphics, 26(1), 98–108. 10.1109/TVCG.2019.2934788
    https://doi.org/10.1109/TVCG.2019.2934788 [Google Scholar]
  31. R Core Team
    R Core Team (2021) R: A Language and Environment for Statistical Computing. Vienna, Austria. R Foundation for Statistical Computing. https://www.R-project.org/
    [Google Scholar]
  32. Ricker, B., Kraak, M.-J., & Engelhardt, Y.
    (2020) 24. The power of visualization choices: Different images of patterns in space. Data Visualization in Society, 4071. 10.1515/9789048543137‑028
    https://doi.org/10.1515/9789048543137-028 [Google Scholar]
  33. Roberts, J. C.
    (2007) State of the art: Coordinated & multiple views in exploratory visualization. Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007), 61–71. 10.1109/CMV.2007.20
    https://doi.org/10.1109/CMV.2007.20 [Google Scholar]
  34. Simpson, J.
    (2020) 10. Visualizing data: A lived experience. Data Visualization in Society, 1571. 10.1515/9789048543137‑014
    https://doi.org/10.1515/9789048543137-014 [Google Scholar]
  35. Skeels, M., Lee, B., Smith, G., & Robertson, G. G.
    (2010) Revealing uncertainty for information visualization. Information Visualization, 9(1), 70–81. 10.1057/ivs.2009.1
    https://doi.org/10.1057/ivs.2009.1 [Google Scholar]
  36. Song, H., & Szafir, D. A.
    (2018) Where’s My Data? Evaluating Visualizations with Missing Data. IEEE Transactions on Visualization and Computer Graphics, 25(1), 914–924. 10.1109/TVCG.2018.2864914
    https://doi.org/10.1109/TVCG.2018.2864914 [Google Scholar]
  37. Urbanek, S.
    (2013) png: Read and write PNG images. https://CRAN.R-project.org/package=png
    [Google Scholar]
  38. van Geenen, D., & Wieringa, M.
    (2020) 9. Approaching data visualizations as interfaces: An empirical demonstration of how data are imag (in) ed. Data Visualization in Society, 1411. 10.2307/j.ctvzgb8c7.15
    https://doi.org/10.2307/j.ctvzgb8c7.15 [Google Scholar]
  39. Wickham, H.
    (2016) ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org. 10.1007/978‑3‑319‑24277‑4
    https://doi.org/10.1007/978-3-319-24277-4 [Google Scholar]
  40. Wickham, H., François, R., Henry, L., & Müller, K.
    (2021) dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr
    [Google Scholar]
  41. Wilke, C. O.
    (2020) cowplot: Streamlined Plot Theme and Plot Annotations for “ggplot2.”https://CRAN.R-project.org/package=cowplot
    [Google Scholar]
  42. Windhager, F., Salisu, S., Schreder, G., & Mayr, E.
    (2018) Orchestrating overviews: A synoptic approach to the visualization of cultural collections. Open Library of Humanities, 4(2). 10.16995/olh.276
    https://doi.org/10.16995/olh.276 [Google Scholar]
  43. Zeileis, A., Fisher, J., Hornik, K., Ihaka, R., McWhite, C., Murrell, P., Stauffer, R., & Wilke, C.
    (2020) colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes. (96th ed., Vol.11). Journal of Statistical Software. 10.18637/jss.v096.i01
    https://doi.org/10.18637/jss.v096.i01 [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1075/idj.22006.hen
Loading
/content/journals/10.1075/idj.22006.hen
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): critical visualization; critique; data studies; urban data; visualization design
This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error